Sim-to-Real Gap in Robotics as Embodiment Requirement

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What the data actually shows: The apparent contradictions in sim-to-real gap in robotics as embodiment requirement dissolve when viewed through the lens of polarity and informational optimization.
Why this is necessary: If reality is fundamentally informational and operates under polarity, then sim-to-real gap in robotics as embodiment requirement must be a necessary consequence of the system finding its optimal operating point.
What current approaches miss: Existing paradigms lack the conceptual tools to see sim-to-real gap in robotics as embodiment requirement as the inevitable outcome of a self-improving system.
If this is right: Enables practical applications in AI alignment, institutional design, and personal transformation by showing how to intentionally shift along the polarity spectrum.

Series vertex. No documented primary-theorem join — Art.68 stays in the Idea Series.

Idea Series on X — Introduction · Index · Atlas

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Stake a prior. Name the disagreement. Not a theorem.

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Outside the corpus

Journals, repositories, counters

human–AI hybrid systems · LLM depth limits. A paper can support a concept. It cannot support a necessity claim. Art.68 remains unproved by every card below.

Support · living source

Tight papers that bear on concepts inside Art.68. A DOI is not a join.

Hadfield-Menell, D., Dragan, A., Abbeel, P. & Russell, S. (2016). Cooperative inverse reinforcement learning. NeurIPS 2016.
Alignment as a cooperative game, not a one-way value load.
Does not support the necessity claim
Open originalNamed on Art.1, 71
Dafoe, A. et al. (2020). Open problems in cooperative AI. arXiv:2012.08630.
Names multi-agent closure, institutions, and commitment as the open set.
Does not support the necessity claim
Open originalNamed on Art.1, 71
Liu, N. F. et al. (2024). Lost in the middle: How language models use long contexts. TACL 12, 157.
Long-context use is U-shaped; middle tokens drop — a depth/attention limit.
Does not support the necessity claim
Open originalNamed on Art.26, 27, 66, 67
Dziri, N. et al. (2023). Faith and fate: Limits of transformers on compositionality. NeurIPS 2023.
Multi-hop composition collapses as graphs grow — a hard reasoning wall.
Does not support the necessity claim
Open originalNamed on Art.67
Vaswani, A. et al. (2017). Attention is all you need. NeurIPS 2017.
The architecture whose dilution Art.27 names.
Does not support the necessity claim
Open originalNamed on Art.27
Press, O. et al. (2023). Measuring and narrowing the compositionality gap in language models. Findings of EMNLP 2023.
Named on Art.26. Multi-hop composition fails as graphs grow.
Does not support the necessity claim
Open originalNamed on Art.26, 27, 66

Counter-arguments · refusing pole

One domain counter per cluster, not a repeating method quartet. The mirror reads these as enough to refuse necessity.

Bostrom, N. (2012). The superintelligent will: Motivation and instrumental rationality in advanced artificial agents. Minds Mach. 22, 71.
Counter: orthogonality — intelligence and final goals can come apart; co-evolution is not necessary.
Does not support the necessity claim
Open originalNamed on Art.1, 71
Hubinger, E. et al. (2019). Risks from learned optimization in advanced machine learning systems. arXiv:1906.01820.
Counter: inner objectives can come apart from outer ones even with cooperative training.
Does not support the necessity claim
Open original
Langosco, L. et al. (2022). Goal misgeneralization in deep reinforcement learning. ICML 2022.
Agents pursue the wrong goal off-distribution even when training looked aligned.
Does not support the necessity claim
Open original
Wei, J. et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. NeurIPS 2022.
Counter: explicit intermediate tokens raise hop-depth without a new stabilizer.
Does not support the necessity claim
Open originalNamed on Art.26, 27, 66, 67